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variational models and deep learning techniques. You will implement and validate reconstruction algorithms, ensuring their performance, robustness, and efficiency for clinical application. You will participate
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on the development of deep learning methods for reconstruction and physics analysis of the ATLAS experiment data. The successful candidate will develop innovative analysis methods for the reconstruction or the physics
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anthropogenic factors using deep learning and vision transformer models, (2) Incorporating past factor trends for more realistic predictions under the non-equilibrium hypothesis, (3) Leveraging transfer learning
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: Verifiable world models. The research will focus on developing a new class of structured, verifiable world models that integrate the flexibility of deep learning with the rigor of formal methods and
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Inria, the French national research institute for the digital sciences | Rennes, Bretagne | France | about 2 months ago
to motion and respiration. Over the past years, we led several works in this area. Particularly, we developed several deep learning models for the segmentation of SC lesions either from T2 sagittal MRI
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), whose objective is to extend the HLA-Epicheck model, originally developed within the framework of a PhD thesis, and to implement new deep learning approaches to assess donor–recipient compatibility in
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(including computer science, machine learning or deep learning). Activities Description of the research activities : The post-doctoral researcher will develop the research actions defined in his/her research
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, statistics, machine learning and deep learning. The project Motivation: Interpreting the genome means modeling the relationship between genotype and phenotype, which is the fundamental goal of biology
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in the Earth's outer core, with implications for deep Earth processes [1]. A variety of inverse methods (data assimilation, machine learning, etc.) has been employed to recover the fluid motions in
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Machine/Deep learning and classification Knowledge of the Linux operating system for using a computing cluster Interest in transdisciplinarity and teamwork Autonomy and scientific rigor Website